• Korea Trade Review
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• v.42 no.2
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• pp.369-390
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• 2017

The view of social responsibility activities from the pioneer studies found that most research is mainly limited to the corporate social responsibility activities. The related studies on the individual level are very few. Therefore, it is very necessary to make a clearer and more systematic empirical research for the global companies whose employees are directly involved in the companies' social responsibility activities. In order to find the relationship between variables, we collected data from chinese employee of Korean firms which located in China. The result of empirical test is as follows; First, the social responsibility activities of the individual level have a significant positive effect on the employees' job satisfaction and organization inputs. In other words, social responsibility activities could improve the employee's job satisfaction and organization inputs. Second, innovative organizational culture of South Korean companies has a significant positive effect on the individual level social responsibility activities. Third, transformational leadership of the CEO in South Korean have no effect on personal level social responsibility activities. Fourth, the CEO'S ethical values have great positive effect on personal level of social responsibility activities. Through the analysis we can see, in the process of global corporate implicating social responsibility activities, the CEO'S ethical values are more important than the transformational leadership of the CEO. Finally, in the relationship between the employees' personal ethical values and personal social responsibility activities, the employees' personal ethical values in South Korean companies have great positive effect on the personal level social responsibility activities.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.711-719
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• 2024

This study examined the policy of opening up the Chinese A-share market and its performance in four aspects: institutional investors system, cross-trading system with overseas stock markets, inclusion of A-shares into global indices, and establishment of a new board. Then, the impact of these policies on the Stock Index was empirically analyzed, and it was confirmed that institutional investors system such as QFII and RQFII, cross-trading system with overseas stock markets such as Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect, inclusion of A-shares into global indices such as the MSCI EM index and FTSE Russell index, and the establishment of a new board of the Science Innovation Board all had statistically significant positive impacts on the stock index. Based on the results of these analysis, we conclude that China should further expand its stock market opening to the outside world, that mutual efforts are needed to alleviate political conflicts and improve understanding, and that easing industry regulations, including real estate, will help China's economic recovery and foreigners' investment in the A-share market.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Journal of Korea Technology Innovation Society
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• v.18 no.3
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• pp.468-491
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• 2015

The major objective of this research is to suggest the proactive strategy and management of public R&D for the active transfer of technology based on the influential factor analysis of technology transfer. This study identified influential factors which make the greatest impact on the success of public technology transfer and commercialization through three points of view-technology supplier's view, technology adopter's view and view of commercialization-which contribute to successful technology transfer and commercialization. The core influential variables for blocking technology transfer are identified such as additional technological development, search for technology adopter followed by mass production technology and testing of confidence. Technology adopter is to create new markets or expand existing markets through the superiority (innovation) of licensing technology, increasing the internal innovation capabilities and maximizing the impacts of technology. This research suggests two effective strategies for improving technology transfer such as technology planning and marketing in the view of technology license. The strategy of technology planning should be established and executed to meet both technology trends and adopter's needs. And strong patents should be secured in terms of licensing of technology. Also the technological performance should be evaluated at mid-term appraisal, confirming the needs of adopter and competitive advantage of technology and patent. In addition to this, the customized technology marketing strategy for different fields of applications is also required in order to improve the likelihood of technology transfer. If the performance of R&D organization could be evaluated by global technological competitiveness and spillover effects of commercialization rather than quantitative output, the flow of technology transfer and commercialization would be accelerated.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.1-9
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• 2010

As the process technology scales down, an interconnection has became a major performance constraint for multi-core processors. Recently, in order to mitigate the performance bottleneck of the interconnection for multi-core processors, a 3D integration technique has drawn quite attention. The 3D integrated multi-core processor has advantage for reducing global wire length, resulting in a performance improvement. However, it causes serious thermal problems due to increased power density. For this reason, to design efficient 3D multi-core processors, thermal-aware design techniques should be considered. In this paper, we analyze the temperature on the 3D multi-core processors in function unit level through various experiments. We also present temperature characteristics by varying application features, cooling characteristics, and frequency levels on 3D multi-core processors. According to our experimental results, following two rules should be obeyed for thermal-aware 3D processor design. First, to optimize the thermal profile of cores, the core with higher cooling efficiency should be clocked at a higher frequency. Second, to lower the temperature of cores, a workload with higher thermal impact should be assigned to the core with higher cooling efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.540-548
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• 2012

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Industry Promotion Research
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• v.4 no.2
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• pp.9-18
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• 2019

This study investigated the relationship between ICT environment and business performance. In the case of Korea's major industries, large corporations have already established and operate a considerable level of smart factories, leading the global market. However, SMEs, which account for 95% of the total companies, are not able to build smart factories themselves. Smart factory construction The total number of government-supported enterprises is 4.891 companies (3,984 companies, 907 companies in construction) 2.9% of factories and 97.1% (166,344 companies) There is a big problem to be improved. The result of this study is that the first research objective of this study, which suggests the theoretical system that the will of the manager is most important for the successful establishment of the smart factory, which is part of the corporate innovation to meet the rapidly changing environment. Second, it can be seen that financing for building a smart factory is a key factor in building a smart factory, as well as funding itself. Third, it was found that besides its own technology, technology support for government and external technology consulting support are very important for smart construction. Fourth, organizational participation of internal organizers showed that cooperative and positive positive participation is also a factor of success. As a follow-up study, we analyzed the cause of the company's operation, analyzed the cause of the problem with the 4M1E technique, developed the countermeasures, and compared it before and after the improvement, standardized the improvement and needed further study. It is meaningful that the study provided basic data for building a smart factory through the analysis of the relationship between the ICT environment and business performance of the company.